Clinical history and management recommendations of the smooth muscle dysfunction syndrome due to ACTA2 arginine 179 alterations

Smooth muscle dysfunction syndrome (SMDS) due to heterozygous ACTA2 arginine 179 alterations is characterized by patent ductus arteriosus, vasculopathy (aneurysm and occlusive lesions), pulmonary arterial hypertension, and other complications in smooth muscle-dependent organs. We sought to define the clinical history of SMDS to develop recommendations for evaluation and management. Medical records of 33 patients with SMDS (median age 12 years) were abstracted and analyzed. All patients had congenital mydriasis and related pupillary abnormalities at birth and presented in infancy with a patent ductus arteriosus or aortopulmonary window. Patients had cerebrovascular disease characterized by small vessel disease (hyperintense periventricular white matter lesions; 95%), intracranial artery stenosis (77%), ischemic strokes (27%), and seizures (18%). Twelve (36%) patients had thoracic aortic aneurysm repair or dissection at median age of 14 years and aortic disease was fully penetrant by the age of 25 years. Three (9%) patients had axillary artery aneurysms complicated by thromboembolic episodes. Nine patients died between the ages of 0.5 and 32 years due to aortic, pulmonary, or stroke complications, or unknown causes. Based on these data, recommendations are provided for the surveillance and management of SMDS to help prevent early-onset life-threatening complications

Identification of in vivo nonlinear anisotropic mechanical properties of ascending thoracic aortic aneurysm from patient-specific CT scans

Accurate identification of in vivo nonlinear, anisotropic mechanical properties of the aortic wall of individual patients remains to be one of the critical challenges in the field of cardiovascular biomechanics. Since only the physiologically loaded states of the aorta are given from in vivo clinical images, inverse approaches, which take into account of the unloaded configuration, are needed for in vivo material parameter identification. Existing inverse methods are computationally expensive, which take days to weeks to complete for a single patient, inhibiting fast feedback for clinicians. Moreover, the current inverse methods have only been evaluated using synthetic data. In this study, we improved our recently developed multi-resolution direct search (MRDS) approach and the computation time cost was reduced to 1~2 hours. Using the improved MRDS approach, we estimated in vivo aortic tissue elastic properties of two ascending thoracic aortic aneurysm (ATAA) patients from pre-operative gated CT scans. For comparison, corresponding surgically-resected aortic wall tissue samples were obtained and subjected to planar biaxial tests. Relatively close matches were achieved for the in vivo-identified and ex vivo-fitted stress-stretch responses. It is hoped that further development of this inverse approach can enable an accurate identification of the in vivo material parameters from in vivo image data

Heavy Metal Content in Thoracic Tissue Samples from Patients with and without NSCLC

Objectives. Environmental factors expose an individual to heavy metals that may stimulate cancer growth preclinically including non-small cell lung cancer (NSCLC) cells. Here, we examine the prevalence of four heavy metals present in postsurgical tissues from individuals with and without NSCLC. Materials and Methods. Thoracic tissue samples from two separate sample sets were analyzed for cadmium (Cd), arsenic (As), mercury (Hg), and lead (Pb) content. Results. In the first sample set, there was no significant measurable amount of Pb and Hg found in either NSCLC tissue or nonmalignant lung tissue samples. Cd was the most prevalent heavy metal and As was present in moderate amounts. In the second sample set, Cd was measurable across all tissue types taken from 28 NSCLC patients and significantly higher Cd was measurable in noncancer benign lung (n=9). In the NSCLC samples, As was measurable in moderate amounts, while Hg and Pb amounts were negligible. Conclusion. Cd and As are present in lung tissues for patients with NSCLC. With existing preclinical evidence of their tumorigenecity, it is plausible that Cd and/or As may have an impact on NSCLC development. Additional studies examining the prevalence and association between smokers and nonsmokers are suggested

Computation of a probabilistic and anisotropic failure metric on the aortic wall using a machine learning-based surrogate model

Scalar-valued failure metrics are commonly used to assess the risk of aortic aneurysm rupture and dissection, which occurs under hypertensive blood pressures brought on by extreme emotional or physical stress. To compute failure metrics under an elevated blood pressure, a classical patient-specific computer model consists of multiple computation steps involving inverse and forward analyses. These classical procedures may be impractical for time-sensitive clinical applications that require prompt feedback to clinicians. In this study, we developed a machine learning-based surrogate model to directly predict a probabilistic and anisotropic failure metric, namely failure probability (FP), on the aortic wall using aorta geometries at the systolic and diastolic phases. Ascending thoracic aortic aneurysm (ATAA) geometries of 60 patients were obtained from their CT scans, and biaxial mechanical testing data of ATAA tissues from 79 patients were collected. Finite element simulations were used to generate datasets for training, validation, and testing of the ML-surrogate model. The testing results demonstrated that the ML-surrogate can compute the maximum FP failure metric, with 0.42% normalized mean absolute error, in 1 s. To compare the performance of the ML-predicted probabilistic FP metric with other isotropic or deterministic metrics, a numerical case study was performed using synthetic “baseline” data. Our results showed that the probabilistic FP metric had more discriminative power than the deterministic Tsai-Hill metric, isotropic maximum principal stress, and aortic diameter criterion. •End-to-end failure metric computation enabled by a machine learning model.•Effect of hyperelastic properties is incorporated using two-phase aorta geometries.•CT images and tissue testing data of real patients are used to generate FEA data.•The FP failure metric can be computed with 0.42% NMAE in 1 s.•The FP have more discriminative power than deterministic or isotropic metrics

A probabilistic and anisotropic failure metric for ascending thoracic aortic aneurysm risk assessment

To noninvasively assess the risk of aneurysm rupture and dissection, an accurate material failure metric of the aortic wall is crucial. Previous studies used deterministic or isotropic failure metrics for the aortic wall. However, experimental studies have shown that aortic wall tensile strengths in circumferential and axial directions are significantly different (i.e., anisotropic) and vary greatly among patients. In this study, we developed a new probabilistic and anisotropic material failure metric for rupture risk assessment of ascending thoracic aortic aneurysm (ATAA). We performed uniaxial tensile failure tests using aortic tissue samples of 84 ATAA patients, from which a joint probability distribution of the anisotropic wall strengths was obtained. Subsequently, we derived an anisotropic failure probability (FP) metric based on the Tsai-Hill (TH) failure criterion. The novel FP metric incorporates uncertainty and anisotropy of failure properties. To compare the FP metric with traditional deterministic and isotropic metrics, we numerically estimated "baseline" risks of additional 41 ATAA patients using matching CT images and tissue testing data. We presented different risk assessment methods (e.g., with and without patient-specific hyperelastic properties) and compared them using receiver operating characteristic (ROC) curves. The results demonstrated that: (1) the probabilistic FP metric outperforms the deterministic TH metric and the isotropic maximum principal stress; (2) patient-specific hyperelastic properties can help to improve the performance of probabilistic FP metric in ATAA risk assessment. The proposed probabilistic modeling framework may be adopted for other types of materials